Representations of epistemic uncertainty and awareness in data-driven strategies Article Swipe
Mario Angelelli
,
Massimiliano Gervasi
,
Enrico Ciavolino
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s00500-024-09661-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s00500-024-09661-8
The diffusion of AI and big data is reshaping decision-making processes by increasing the amount of information that supports decisions, while reducing direct interaction with data and empirical evidence. This paradigm shift introduces new sources of uncertainty, as limited data observability results in ambiguity and a lack of interpretability. The need for the proper analysis of data-driven strategies motivates the search for new models that can describe this type of bounded access to knowledge.This contribution presents a novel theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We provide a dynamical description of knowledge states by endowing our model with a structure to compare and combine them. Specifically, an update is represented through combinations, and its explainability is based on its consistency in different dimensional representations. We look at inequivalent knowledge representations in terms of multiplicity of inferences, preference relations, and information measures. Furthermore, we define a formal analogy with two scenarios that illustrate non-classical uncertainty in terms of ambiguity (Ellsberg’s model) and reasoning about knowledge mediated by other agents observing data (Wigner’s Friend). Finally, we discuss some implications of the proposed model for data-driven strategies, with special attention to reasoning under uncertainty about business value dimensions and the design of measurement tools for their assessment.
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- Language
- en
- Landing Page
- https://doi.org/10.1007/s00500-024-09661-8
- OA Status
- hybrid
- Cited By
- 11
- References
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https://doi.org/10.1007/s00500-024-09661-8Digital Object Identifier
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Representations of epistemic uncertainty and awareness in data-driven strategiesWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-07-29Full publication date if available
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Mario Angelelli, Massimiliano Gervasi, Enrico CiavolinoList of authors in order
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https://doi.org/10.1007/s00500-024-09661-8Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1007/s00500-024-09661-8Direct OA link when available
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Computer science, Uncertainty quantification, Epistemology, Artificial intelligence, Psychology, Knowledge management, Machine learning, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 7, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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